1. The Field of the Invention
The present invention relates to targeted advertising. More specifically, the present invention relates to systems, methods, and computer program products for modifying an advertisement score based on a probability that a user will respond to the advertisement, the advertisement score being indicative of whether the advertisement should be presented.
2. Background and Relevant Art
Advertisers often present advertisements to users of networked computer systems (e.g., Internet-connected computer systems) in hopes that the users of the networked computer systems will become interested in the advertised products. At times, advertisers may present advertisements that are viewed by users and as a result generate user interest in the advertised product. However, at other times, and perhaps more frequently, viewed advertisements generate little, if any, user interest in advertised products. In some cases, users simply ignore advertisements, not viewing them at all.
In the past, the reduced effectiveness of advertisements presented on computer networks was in part due to advertisers having reduced amounts of contextual data associated with possible advertisement recipients. In a broadcast or cable television environment, an advertiser may, at the very least, have contextual data on the channel that will present an advertisement. In many cases, an advertiser will also have contextual data on the programming and time of day during which an advertisement will be presented. However, computer networks, such as the Internet, may include voluminous amounts of information, only a small portion of which may be of interest to a particular user. An advertiser may have had no way to determine what a particular user is interested in and thus present appropriate advertisements.
As such, a variety of advertising techniques have been developed to “target” users on a computer network. These targeting techniques are designed to present advertisements that, if viewed, have increased chances of generating user interest in an advertised product. Conventional targeting techniques often associate advertisements with advertisement scores, where advertisements with higher scores are presented to a user before advertisements with lower scores. An advertising server may generate a score for a number of advertisements and then present the advertisements with the higher scores to a user.
An advertisement server may use deterministic rules when generating advertisement scores. Each advertisement may begin with a base score that is modified as successive rules are applied. A deterministic rule may be, for example, “if a user is less than age 30, then increase the score for this advertisement.” The advertisement server may access user data, for example, data contained in a user profile, to determine how rules are applied. If the advertisement server accessed user data indicating that a particular user is age 25, application of the previous rule would result in an increase in associated advertisement scores.
A series of rules may be applied based on different portions of user data, for example, age, sex, and income, to cause an advertisement score for a particular group of users to be increased or decreased. This is beneficial, as an advertiser may configure a series of rules to increase advertisement scores for particular groups of users the advertiser believes are more likely to be interested in a particular product. Likewise, an advertiser may configure a series of rules to decrease advertisement scores for particular groups of users the advertiser believes are less likely to be interested in a particular product.
Current targeting techniques are beneficial for increasing the chances of presenting advertisements that will generate user interest. However, current targeting techniques fail to consider the probability that a potentially interested user will actually respond to an advertisement by buying the advertised product or selecting the advertisement (“clicking through”) to view additional information. For example, it may be that a user is interested in an advertised product but for some reason has a decreased probability of responding to an advertisement associated with the product. Presenting advertisements to users who have decreased probabilities of responding to the advertisements results in inefficient use of advertisement server resources. Additionally, a user with a reduced probability for responding to an advertisement may find presentation of such an advertisement undesirable.
Therefore, what are desired are systems, methods, and computer program products, for modifying an advertisement score based on a probability that a user will respond to the advertisement.